Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma

Chen, Zhongjian; Gao, Yun; Huang, Xiancong; Yao, Yao; Chen, Keke; Zeng, Su; Mao, Weimin

doi:10.1016/j.jpba.2021.113937

Cited by 16 publications

(14 citation statements)

References 17 publications

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“…The clinicopathological parameters of ESCC patients were collected. Immunohistochemistry (IHC) staining was performed to investigate the expression of DDX51 in ESCC tissues and adjacent normal tissues, and the experimental procedure is described in a previous report[ 14 ].…”

Section: Methodsmentioning

confidence: 99%

Knockdown of DEAD-box 51 inhibits tumor growth of esophageal squamous cell carcinoma via the PI3K/AKT pathway

Hu¹,

Sun²,

Xu³

et al. 2022

WJG

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BACKGROUND Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent malignancies that seriously threaten people’s health worldwide. DEAD-box helicase 51 (DDX51) is a member of the DEAD-box (DDX) RNA helicase family, and drives or inhibits tumor progression in multiple cancer types. AIM To determine whether DDX51 affects the biological behavior of ESCC. METHODS The expression of DDX51 in ESCC tumor tissues and adjacent normal tissues was detected by Immunohistochemistry (IHC) analyses and quantitative PCR (qPCR). We knocked down DDX51 in ESCC cell lines by using a small interfering RNA (siRNA) transfection. The proliferation, apoptosis, and mobility of DDX51 siRNA-transfected cells were detected. The effect of DDX51 on the phosphoinositide 3-kinase (PI3K)/AKT pathway was investigated by western blot analysis. A mouse xenograft model was established to investigate the effects of DDX51 knockdown on ESCC tumor growth. RESULTS DDX51 exhibited high expression in ESCC tissues compared with normal tissues and represented a poor prognosis in patients with ESCC. Knockdown of DDX51 induced inhibition of ESCC cell proliferation and promoted apoptosis. Moreover, DDX51 siRNA-expressing cells also exhibited lower migration and invasion rates. Investigations into the underlying mechanisms suggested that DDX51 knockdown induced inactivation of the PI3K/AKT pathway, including decreased phosphorylation levels of phosphate and tensin homolog, PI3K, AKT, and mammalian target of rapamycin. Rescue experiments demonstrated that the AKT activator insulin-like growth factor 1 could reverse the inhibitory effects of DDX51 on ESCC malignant development. Finally, we injected DDX51 siRNA-transfected TE-1 cells into an animal model, which resulted in slower tumor growth. CONCLUSION Our study suggests for the first time that DDX51 promotes cancer cell proliferation by regulating the PI3K/AKT pathway; thus, DDX51 might be a therapeutic target for ESCC.

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Section: Methodsmentioning

confidence: 99%

Knockdown of DEAD-box 51 inhibits tumor growth of esophageal squamous cell carcinoma via the PI3K/AKT pathway

Hu¹,

Sun²,

Xu³

et al. 2022

WJG

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“…RF and SVM also demonstrated favorable results in the identification of esophageal squamous cell carcinoma tissue based on differential metabolites (Z. Chen et al, 2021 ). Among the three models evaluated, RF had the highest predictive performance (100%), but required more computational time (8.99 s), compared to PLS and SVM models, which showed similar predictive performance (95%) and similar computational time (1.27 s and 1.11 s).…”

Section: Cancermentioning

confidence: 99%

“…Shen et al, 2018), L1 norm SVM (Zhou et al, 2010) (Guan et al, 2009) (Zhou et al, 2010) and variable importance in projection (VIP) ( (Zhang et al, 2018), (Cheng et al, 2019) (Z. Chen et al, 2021)).…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

“…Popular feature selection methods used with SVM models in metabolomics studies include recursive feature elimination (RFE) ( Guan et al, 2009 ; R. Shen et al, 2018 ), L1 norm SVM ( Zhou et al, 2010 ) ( Guan et al, 2009 ) ( Zhou et al, 2010 ) and variable importance in projection (VIP) (( Zhang et al, 2018 ), ( Cheng et al, 2019 ) (Z. Chen et al, 2021 )).…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

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Applications of machine learning in metabolomics: Disease modeling and classification

Galal

Talal²,

Moustafa³

2022

Front. Genet.

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Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

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“…Lastly, the integration of microbiome, proteomic and/or genomic data sets is also critical in tissue metabolomic studies to better validate the putative clinical utility of prognostic or diagnostic biomarkers, as well as exploring their likely causative role in disease pathophysiology as demonstrated by the accumulation of hydroxybutyric acid metabolites in ovarian cancer [ 146 ]. This strategy can also lead to the identification of new potential therapeutic targets for treating cancers with poor survivorship, such as esophageal squamous cell carcinoma [ 147 ]. Nevertheless, future discovery-based tissue metabolomic studies are recommended to incorporate rigorous study designs that are replicated independently in representative populations using complementary analytical methods to demonstrate their reproducibility, diagnostic accuracy, and overall clinical utility based their estimated health impact [ 148 ].…”

Section: Current Challenges In Tissue Metabolomics: Future Directionsmentioning

confidence: 99%

New Advances in Tissue Metabolomics: A Review

Saoi

Britz‐McKibbin

2021

Metabolites

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Metabolomics offers a hypothesis-generating approach for biomarker discovery in clinical medicine while also providing better understanding of the underlying mechanisms of chronic diseases. Clinical metabolomic studies largely rely on human biofluids (e.g., plasma, urine) as a more convenient specimen type for investigation. However, biofluids are non-organ specific reflecting complex biochemical processes throughout the body, which may complicate biochemical interpretations. For these reasons, tissue metabolomic studies enable deeper insights into aberrant metabolism occurring at the direct site of disease pathogenesis. This review highlights new advances in metabolomics for ex vivo analysis, as well as in situ imaging of tissue specimens, including diverse tissue types from animal models and human participants. Moreover, we discuss key pre-analytical and post-analytical challenges in tissue metabolomics for robust biomarker discovery with a focus on new methodological advances introduced over the past six years, including innovative clinical applications for improved screening, diagnostic testing, and therapeutic interventions for cancer.

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Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma

Cited by 16 publications

References 17 publications

Knockdown of DEAD-box 51 inhibits tumor growth of esophageal squamous cell carcinoma via the PI3K/AKT pathway

Knockdown of DEAD-box 51 inhibits tumor growth of esophageal squamous cell carcinoma via the PI3K/AKT pathway

Applications of machine learning in metabolomics: Disease modeling and classification

New Advances in Tissue Metabolomics: A Review

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